| Federated Learning with Matched Averaging H Wang, M Yurochkin, Y Sun, D Papailiopoulos, Y Khazaeni ICLR 2020 - International Conference on Learning Representations, 2020 | 341 | 2020 |
| Atomo: Communication-efficient learning via atomic sparsification H Wang, S Sievert, S Liu, Z Charles, D Papailiopoulos, S Wright Advances in Neural Information Processing Systems 31, 2018 | 234 | 2018 |
| Draco: Byzantine-resilient distributed training via redundant gradients L Chen, H Wang, Z Charles, D Papailiopoulos International Conference on Machine Learning, 903-912, 2018 | 197* | 2018 |
| Fedml: A research library and benchmark for federated machine learning C He, S Li, J So, X Zeng, M Zhang, H Wang, X Wang, P Vepakomma, ... arXiv preprint arXiv:2007.13518, 2020 | 169* | 2020 |
| Attack of the tails: Yes, you really can backdoor federated learning H Wang, K Sreenivasan, S Rajput, H Vishwakarma, S Agarwal, J Sohn, ... Advances in Neural Information Processing Systems 33, 16070-16084, 2020 | 136 | 2020 |
| A field guide to federated optimization J Wang, Z Charles, Z Xu, G Joshi, HB McMahan, M Al-Shedivat, G Andrew, ... arXiv preprint arXiv:2107.06917, 2021 | 96 | 2021 |
| DETOX: A redundancy-based framework for faster and more robust gradient aggregation S Rajput, H Wang, Z Charles, D Papailiopoulos Advances in Neural Information Processing Systems 32, 2019 | 69 | 2019 |
| ErasureHead: Distributed Gradient Descent without Delays Using Approximate Gradient Coding H Wang, Z Charles, D Papailiopoulos https://arxiv.org/abs/1901.09671, 2019 | 46* | 2019 |
| The effect of network width on the performance of large-batch training L Chen, H Wang, J Zhao, D Papailiopoulos, P Koutris Advances in Neural Information Processing Systems 31, 2018 | 18 | 2018 |
| Adaptive Gradient Communication via Critical Learning Regime Identification S Agarwal, H Wang, K Lee, S Venkataraman, D Papailiopoulos Proceedings of Machine Learning and Systems 3, 55-80, 2021 | 11 | 2021 |
| On the utility of gradient compression in distributed training systems S Agarwal, H Wang, S Venkataraman, D Papailiopoulos Proceedings of Machine Learning and Systems 4, 652-672, 2022 | 10 | 2022 |
| Pufferfish: communication-efficient models at no extra cost H Wang, S Agarwal, D Papailiopoulos Proceedings of Machine Learning and Systems 3, 365-386, 2021 | 9 | 2021 |
| Recognizing actions during tactile manipulations through force sensing G Subramani, D Rakita, H Wang, J Black, M Zinn, M Gleicher 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2017 | 5 | 2017 |
| Avoiding negative transfer on a focused task with deep multi-task reinforcement learning YLAGS Liu, H Wang, Y Liang, A Gitter | 2 | 2017 |
| Rare Gems: Finding Lottery Tickets at Initialization K Sreenivasan, J Sohn, L Yang, M Grinde, A Nagle, H Wang, K Lee, ... arXiv preprint arXiv:2202.12002, 2022 | 1 | 2022 |
| Solon: Communication-efficient Byzantine-resilient Distributed Training via Redundant Gradients L Chen, L Chen, H Wang, S Davidson, E Dobriban arXiv preprint arXiv:2110.01595, 2021 | | 2021 |
| Demonstration of Nimbus: Model-based Pricing for Machine Learning in a Data Marketplace L Chen, H Wang, L Chen, P Koutris, A Kumar Proceedings of the 2019 International Conference on Management of Data, 1885 …, 2019 | | 2019 |
| Convergence and Runtime of Approximate Gradient Coded Gradient Descent H Wang, Z Charles, D Papailiopoulos | | |
Dimitris PapailiopoulosAssistant Professor, University of Wisconsin-Madisonpapail.io의 이메일 확인됨
